Holmes and the Bald Man: Why Rule of Reason Should Be the Standard in Sherman Act Section 2 Cases

[Excerpt] It has been argued that the antitrust laws’ legislative history supports the notion that the laws were meant to prohibit anticompetitive price cuts – regardless of whether they are below cost. Thus, predatory pricing claims used to turn simply on whether the allegedly predatory price was intended to harm rivals. In fact, liability for predatory price discrimination was found without requiring probable or actual monopolization. Yet some cases brought early under Section 2 suggest that below cost pricing was indicative of, if not proof of, the type of conduct Section 2 prohibits. The results under this old scheme were mixed.

Antitrust Standing of Target Corporations to Enjoin Hostile Takeovers Under Section 16 of the Clayton Act

Joint understanding of video and language is an active research area with many applications. Prior work in this domain typically relies on learning text-video embeddings. One difficulty with this approach, however, is the lack of large-scale annotated video-caption datasets for training. To address this issue, we aim at learning text-video embeddings from heterogeneous data sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model with ability to handle missing input modalities during training. As a result, our framework can learn improved text-video embeddings simultaneously from image and video datasets. We also show the generalization of MEE to other input modalities such as face descriptors. We evaluate our method on the task of video retrieval and report results for the MPII Movie Description and MSR-VTT datasets. The proposed MEE model demonstrates significant improvements and outperforms previously reported methods on both text-to-video and video-to-text retrieval tasks